Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [16]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [17]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[17]:
<matplotlib.image.AxesImage at 0x7f2d569f9908>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [18]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[18]:
<matplotlib.image.AxesImage at 0x7f2d569723c8>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [19]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.4.1
Default GPU Device: /device:GPU:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [20]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    
    real_input = tf.placeholder(tf.float32, shape=(None, image_width, image_height, image_channels), name="real_input")
    z_input = tf.placeholder(tf.float32, shape=(None,z_dim), name="z_input")
    learning_rate = tf.placeholder(tf.float32, name="learning_rate")
    
    return real_input , z_input ,learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [73]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    alpha = 0.2
    
    with tf.variable_scope("discriminator",reuse=reuse) :
        # Input layer is 28x28x3
        ip_layer = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        ip_leaky_relu = tf.maximum(alpha * ip_layer, ip_layer)
        # 14x14x64
        
        conv_layer_1 = tf.layers.conv2d(ip_leaky_relu, 128, 5, strides=2, padding='same')
        bn_layer_1 = tf.layers.batch_normalization(conv_layer_1, training=True)
        conv_layer_1_leaky_relu = tf.maximum(alpha * bn_layer_1, bn_layer_1)
        # 7x7x128
        
        conv_layer_2 = tf.layers.conv2d(conv_layer_1_leaky_relu, 256, 5, strides=2, padding='same')
        bn_layer_2 = tf.layers.batch_normalization(conv_layer_2, training=True)
        conv_layer_2_leaky_relu = tf.maximum(alpha * bn_layer_2, bn_layer_2)
        # 4x4x256

        # Flatten it
        flat = tf.reshape(conv_layer_2_leaky_relu, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
        return out, logits

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [67]:
def generator(z, out_channel_dim,is_train=True,beta1=0.01):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    alpha = 0.2
    
    with tf.variable_scope("generator",reuse=not is_train) :
        # First fully connected layer
        ip_layer = tf.layers.dense(z, 7*7*512)
        
        # Reshape it to start the convolutional stack
        ip_layer = tf.reshape(ip_layer, (-1, 7, 7, 512))
        ip_bn_layer = tf.layers.batch_normalization(ip_layer, training=is_train)
        ip_leaky_relu = tf.maximum(alpha * ip_bn_layer, ip_bn_layer)
        # 7x7x512 now
        
        conv_layer_1 = tf.layers.conv2d_transpose(ip_leaky_relu, 256, 5, strides=1, padding='same')
        bn_layer_1 = tf.layers.batch_normalization(conv_layer_1, training=is_train)
        conv_layer_1_leaky_relu = tf.maximum(alpha * bn_layer_1, bn_layer_1)
        # 7x7x256 now
        
        conv_layer_2 = tf.layers.conv2d_transpose(conv_layer_1_leaky_relu, 128, 5, strides=2, padding='same')
        bn_layer_2 = tf.layers.batch_normalization(conv_layer_2, training=is_train)
        conv_layer_2_leaky_relu = tf.maximum(alpha * bn_layer_2, bn_layer_2)
        # 14x14x128 now
        
        conv_layer_3 = tf.layers.conv2d_transpose(conv_layer_2_leaky_relu, 64, 5, strides=2, padding='same')
        bn_layer_3 = tf.layers.batch_normalization(conv_layer_3, training=is_train)
        conv_layer_3_leaky_relu = tf.maximum(alpha * bn_layer_3, bn_layer_3)
        # 28x28x64 now
        
        # Output layer
        logits = tf.layers.conv2d_transpose(conv_layer_3_leaky_relu, out_channel_dim, 5, strides=1, padding='same')
        # 28x28x3 now
        
        out = tf.tanh(logits)
        
        return out

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [68]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
        
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    d_loss_real = tf.reduce_mean(
                      tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, 
                                                              labels=tf.ones_like(d_model_real) * 0.9))
    d_loss_fake = tf.reduce_mean(
                      tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, 
                                                              labels=tf.zeros_like(d_model_fake)))
    d_loss = d_loss_real + d_loss_fake

    g_loss = tf.reduce_mean(
                    tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
                                                         labels=tf.ones_like(d_model_fake)))
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [69]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [70]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [71]:
from time import time
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # set Tensorboard logging dir
    logging_path = "logs/{}".format(time())
    
    # parse input parameters
    width, height, channel = data_shape[1], data_shape[2], data_shape[3]
    
    # get model tensors
    input_real, input_z, learn_rate = model_inputs(width, height, channel, z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, channel)
    d_opt, g_opt = model_opt(d_loss, g_loss, learn_rate, beta1)
    
    # create model saver object
    saver = tf.train.Saver()
    
    # create a summary for our cost and accuracy
    tf.summary.scalar("discriminator_loss", d_loss)
    tf.summary.scalar("generator_loss", g_loss)
    
    # merge all summaries into a single "operation" which we can execute in a session 
    summary_op = tf.summary.merge_all()
    
    steps = 0
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        
        # create log writer object
        writer = tf.summary.FileWriter(logging_path,sess.graph,filename_suffix="log",flush_secs=2)
        
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                steps += 1

                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, learn_rate: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_z: batch_z, input_real: batch_images, learn_rate: learning_rate})
                
                # Run summary generation
                summary = sess.run(summary_op, feed_dict={input_z: batch_z, input_real: batch_images, learn_rate: learning_rate})
                
                # write log
                writer.add_summary(summary,steps)
                
                if steps % 100 == 0:
                    # print the losses every 100 steps
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}..., step {}".format(epoch_i+1, epochs, steps),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                if steps % 100 == 0:
                    show_generator_output(sess, 16, input_z, channel, data_image_mode)

        saver.save(sess, './generator.ckpt')

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [76]:
batch_size = 10
z_dim = 100
learning_rate = 0.001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2..., step 100 Discriminator Loss: 2.1506... Generator Loss: 0.3741
Epoch 1/2..., step 200 Discriminator Loss: 0.4195... Generator Loss: 7.6449
Epoch 1/2..., step 300 Discriminator Loss: 2.8040... Generator Loss: 6.3086
Epoch 1/2..., step 400 Discriminator Loss: 0.8511... Generator Loss: 1.1753
Epoch 1/2..., step 500 Discriminator Loss: 3.5983... Generator Loss: 0.0439
Epoch 1/2..., step 600 Discriminator Loss: 1.5937... Generator Loss: 3.0237
Epoch 1/2..., step 700 Discriminator Loss: 0.6202... Generator Loss: 1.4820
Epoch 1/2..., step 800 Discriminator Loss: 0.5233... Generator Loss: 2.4080
Epoch 1/2..., step 900 Discriminator Loss: 0.8644... Generator Loss: 1.2183
Epoch 1/2..., step 1000 Discriminator Loss: 0.8933... Generator Loss: 4.7415
Epoch 1/2..., step 1100 Discriminator Loss: 0.6057... Generator Loss: 1.6136
Epoch 1/2..., step 1200 Discriminator Loss: 0.3502... Generator Loss: 4.2451
Epoch 1/2..., step 1300 Discriminator Loss: 0.5024... Generator Loss: 5.1196
Epoch 1/2..., step 1400 Discriminator Loss: 0.5380... Generator Loss: 2.2608
Epoch 1/2..., step 1500 Discriminator Loss: 0.4122... Generator Loss: 3.5584
Epoch 1/2..., step 1600 Discriminator Loss: 0.6794... Generator Loss: 1.6752
Epoch 1/2..., step 1700 Discriminator Loss: 0.8736... Generator Loss: 5.3528
Epoch 1/2..., step 1800 Discriminator Loss: 0.5925... Generator Loss: 1.9449
Epoch 1/2..., step 1900 Discriminator Loss: 0.4231... Generator Loss: 2.8490
Epoch 1/2..., step 2000 Discriminator Loss: 1.1038... Generator Loss: 0.7013
Epoch 1/2..., step 2100 Discriminator Loss: 0.8799... Generator Loss: 2.8461
Epoch 1/2..., step 2200 Discriminator Loss: 1.0159... Generator Loss: 0.8622
Epoch 1/2..., step 2300 Discriminator Loss: 0.7872... Generator Loss: 1.2272
Epoch 1/2..., step 2400 Discriminator Loss: 0.9275... Generator Loss: 4.2283
Epoch 1/2..., step 2500 Discriminator Loss: 1.0922... Generator Loss: 0.7599
Epoch 1/2..., step 2600 Discriminator Loss: 0.5624... Generator Loss: 1.8684
Epoch 1/2..., step 2700 Discriminator Loss: 0.6405... Generator Loss: 2.5183
Epoch 1/2..., step 2800 Discriminator Loss: 0.3697... Generator Loss: 4.1214
Epoch 1/2..., step 2900 Discriminator Loss: 1.7353... Generator Loss: 0.4765
Epoch 1/2..., step 3000 Discriminator Loss: 0.4591... Generator Loss: 2.4191
Epoch 1/2..., step 3100 Discriminator Loss: 2.0147... Generator Loss: 3.8882
Epoch 1/2..., step 3200 Discriminator Loss: 0.6603... Generator Loss: 1.5283
Epoch 1/2..., step 3300 Discriminator Loss: 0.7675... Generator Loss: 1.5346
Epoch 1/2..., step 3400 Discriminator Loss: 1.1304... Generator Loss: 0.6821
Epoch 1/2..., step 3500 Discriminator Loss: 1.8422... Generator Loss: 0.3309
Epoch 1/2..., step 3600 Discriminator Loss: 0.4119... Generator Loss: 3.5160
Epoch 1/2..., step 3700 Discriminator Loss: 0.6727... Generator Loss: 1.5027
Epoch 1/2..., step 3800 Discriminator Loss: 0.7322... Generator Loss: 1.3473
Epoch 1/2..., step 3900 Discriminator Loss: 0.4813... Generator Loss: 2.5619
Epoch 1/2..., step 4000 Discriminator Loss: 2.7020... Generator Loss: 2.3625
Epoch 1/2..., step 4100 Discriminator Loss: 0.7691... Generator Loss: 1.4289
Epoch 1/2..., step 4200 Discriminator Loss: 1.3105... Generator Loss: 0.6379
Epoch 1/2..., step 4300 Discriminator Loss: 0.5754... Generator Loss: 1.8808
Epoch 1/2..., step 4400 Discriminator Loss: 0.6222... Generator Loss: 1.7636
Epoch 1/2..., step 4500 Discriminator Loss: 0.4869... Generator Loss: 2.2125
Epoch 1/2..., step 4600 Discriminator Loss: 1.6223... Generator Loss: 0.3648
Epoch 1/2..., step 4700 Discriminator Loss: 1.7034... Generator Loss: 0.3896
Epoch 1/2..., step 4800 Discriminator Loss: 1.0504... Generator Loss: 0.7874
Epoch 1/2..., step 4900 Discriminator Loss: 0.4641... Generator Loss: 2.5469
Epoch 1/2..., step 5000 Discriminator Loss: 0.8623... Generator Loss: 1.3065
Epoch 1/2..., step 5100 Discriminator Loss: 1.4892... Generator Loss: 0.5329
Epoch 1/2..., step 5200 Discriminator Loss: 1.2740... Generator Loss: 0.7423
Epoch 1/2..., step 5300 Discriminator Loss: 0.3896... Generator Loss: 3.7978
Epoch 1/2..., step 5400 Discriminator Loss: 0.7747... Generator Loss: 3.5829
Epoch 1/2..., step 5500 Discriminator Loss: 0.6875... Generator Loss: 1.6580
Epoch 1/2..., step 5600 Discriminator Loss: 0.7943... Generator Loss: 1.1139
Epoch 1/2..., step 5700 Discriminator Loss: 0.5885... Generator Loss: 2.3664
Epoch 1/2..., step 5800 Discriminator Loss: 0.7417... Generator Loss: 1.2776
Epoch 1/2..., step 5900 Discriminator Loss: 1.2964... Generator Loss: 0.5987
Epoch 1/2..., step 6000 Discriminator Loss: 0.5416... Generator Loss: 1.9638
Epoch 2/2..., step 6100 Discriminator Loss: 1.2721... Generator Loss: 0.7880
Epoch 2/2..., step 6200 Discriminator Loss: 0.5734... Generator Loss: 3.1047
Epoch 2/2..., step 6300 Discriminator Loss: 0.6121... Generator Loss: 1.7796
Epoch 2/2..., step 6400 Discriminator Loss: 0.7501... Generator Loss: 1.7444
Epoch 2/2..., step 6500 Discriminator Loss: 1.2070... Generator Loss: 0.8278
Epoch 2/2..., step 6600 Discriminator Loss: 1.9790... Generator Loss: 4.0767
Epoch 2/2..., step 6700 Discriminator Loss: 0.9257... Generator Loss: 2.0187
Epoch 2/2..., step 6800 Discriminator Loss: 0.4368... Generator Loss: 2.8687
Epoch 2/2..., step 6900 Discriminator Loss: 0.8212... Generator Loss: 1.1727
Epoch 2/2..., step 7000 Discriminator Loss: 0.4861... Generator Loss: 3.7872
Epoch 2/2..., step 7100 Discriminator Loss: 0.5249... Generator Loss: 2.1308
Epoch 2/2..., step 7200 Discriminator Loss: 0.4792... Generator Loss: 2.2517
Epoch 2/2..., step 7300 Discriminator Loss: 0.8007... Generator Loss: 1.6190
Epoch 2/2..., step 7400 Discriminator Loss: 1.1055... Generator Loss: 1.0872
Epoch 2/2..., step 7500 Discriminator Loss: 1.0468... Generator Loss: 0.9171
Epoch 2/2..., step 7600 Discriminator Loss: 0.5375... Generator Loss: 2.2748
Epoch 2/2..., step 7700 Discriminator Loss: 0.8108... Generator Loss: 1.1566
Epoch 2/2..., step 7800 Discriminator Loss: 0.8504... Generator Loss: 1.1097
Epoch 2/2..., step 7900 Discriminator Loss: 0.4227... Generator Loss: 2.7573
Epoch 2/2..., step 8000 Discriminator Loss: 2.7435... Generator Loss: 0.1581
Epoch 2/2..., step 8100 Discriminator Loss: 0.8974... Generator Loss: 1.0133
Epoch 2/2..., step 8200 Discriminator Loss: 1.2772... Generator Loss: 0.5614
Epoch 2/2..., step 8300 Discriminator Loss: 0.4150... Generator Loss: 3.7390
Epoch 2/2..., step 8400 Discriminator Loss: 0.7572... Generator Loss: 1.1617
Epoch 2/2..., step 8500 Discriminator Loss: 0.6363... Generator Loss: 1.5635
Epoch 2/2..., step 8600 Discriminator Loss: 0.4692... Generator Loss: 2.5135
Epoch 2/2..., step 8700 Discriminator Loss: 0.6334... Generator Loss: 2.0258
Epoch 2/2..., step 8800 Discriminator Loss: 0.8507... Generator Loss: 1.4151
Epoch 2/2..., step 8900 Discriminator Loss: 0.7111... Generator Loss: 1.5641
Epoch 2/2..., step 9000 Discriminator Loss: 0.7390... Generator Loss: 1.8206
Epoch 2/2..., step 9100 Discriminator Loss: 0.8674... Generator Loss: 1.3144
Epoch 2/2..., step 9200 Discriminator Loss: 0.8538... Generator Loss: 1.0378
Epoch 2/2..., step 9300 Discriminator Loss: 0.6378... Generator Loss: 2.4850
Epoch 2/2..., step 9400 Discriminator Loss: 0.9234... Generator Loss: 1.0521
Epoch 2/2..., step 9500 Discriminator Loss: 0.5108... Generator Loss: 2.2666
Epoch 2/2..., step 9600 Discriminator Loss: 0.4617... Generator Loss: 3.4737
Epoch 2/2..., step 9700 Discriminator Loss: 1.5243... Generator Loss: 0.5824
Epoch 2/2..., step 9800 Discriminator Loss: 0.7548... Generator Loss: 1.4091
Epoch 2/2..., step 9900 Discriminator Loss: 1.0204... Generator Loss: 0.9838
Epoch 2/2..., step 10000 Discriminator Loss: 0.7826... Generator Loss: 1.6249
Epoch 2/2..., step 10100 Discriminator Loss: 0.6341... Generator Loss: 1.5092
Epoch 2/2..., step 10200 Discriminator Loss: 0.6313... Generator Loss: 1.6552
Epoch 2/2..., step 10300 Discriminator Loss: 0.5433... Generator Loss: 1.7713
Epoch 2/2..., step 10400 Discriminator Loss: 1.7154... Generator Loss: 0.3430
Epoch 2/2..., step 10500 Discriminator Loss: 0.3913... Generator Loss: 3.4102
Epoch 2/2..., step 10600 Discriminator Loss: 1.0967... Generator Loss: 0.9562
Epoch 2/2..., step 10700 Discriminator Loss: 0.4885... Generator Loss: 2.4142
Epoch 2/2..., step 10800 Discriminator Loss: 1.3237... Generator Loss: 0.6075
Epoch 2/2..., step 10900 Discriminator Loss: 0.9086... Generator Loss: 0.9994
Epoch 2/2..., step 11000 Discriminator Loss: 0.5513... Generator Loss: 1.8696
Epoch 2/2..., step 11100 Discriminator Loss: 0.8843... Generator Loss: 1.0267
Epoch 2/2..., step 11200 Discriminator Loss: 0.7550... Generator Loss: 1.3232
Epoch 2/2..., step 11300 Discriminator Loss: 0.7424... Generator Loss: 1.4231
Epoch 2/2..., step 11400 Discriminator Loss: 0.5974... Generator Loss: 1.7105
Epoch 2/2..., step 11500 Discriminator Loss: 1.3102... Generator Loss: 0.5490
Epoch 2/2..., step 11600 Discriminator Loss: 1.4745... Generator Loss: 0.5520
Epoch 2/2..., step 11700 Discriminator Loss: 0.4141... Generator Loss: 2.8569
Epoch 2/2..., step 11800 Discriminator Loss: 0.8418... Generator Loss: 1.1992
Epoch 2/2..., step 11900 Discriminator Loss: 0.9751... Generator Loss: 0.9759
Epoch 2/2..., step 12000 Discriminator Loss: 0.4323... Generator Loss: 2.8493

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [77]:
batch_size = 64
z_dim = 100
learning_rate = 0.001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/2..., step 100 Discriminator Loss: 1.5427... Generator Loss: 4.0377
Epoch 1/2..., step 200 Discriminator Loss: 0.8330... Generator Loss: 4.0521
Epoch 1/2..., step 300 Discriminator Loss: 1.0016... Generator Loss: 1.3263
Epoch 1/2..., step 400 Discriminator Loss: 3.6439... Generator Loss: 0.1698
Epoch 1/2..., step 500 Discriminator Loss: 0.8808... Generator Loss: 4.6081
Epoch 1/2..., step 600 Discriminator Loss: 0.3413... Generator Loss: 5.3084
Epoch 1/2..., step 700 Discriminator Loss: 0.8331... Generator Loss: 1.7919
Epoch 1/2..., step 800 Discriminator Loss: 2.6548... Generator Loss: 4.6141
Epoch 1/2..., step 900 Discriminator Loss: 1.3607... Generator Loss: 0.6500
Epoch 1/2..., step 1000 Discriminator Loss: 1.1836... Generator Loss: 1.2584
Epoch 1/2..., step 1100 Discriminator Loss: 1.0834... Generator Loss: 1.1559
Epoch 1/2..., step 1200 Discriminator Loss: 1.1915... Generator Loss: 1.1720
Epoch 1/2..., step 1300 Discriminator Loss: 1.0019... Generator Loss: 1.0539
Epoch 1/2..., step 1400 Discriminator Loss: 1.1668... Generator Loss: 0.7941
Epoch 1/2..., step 1500 Discriminator Loss: 1.2077... Generator Loss: 1.2285
Epoch 1/2..., step 1600 Discriminator Loss: 1.3315... Generator Loss: 0.6794
Epoch 1/2..., step 1700 Discriminator Loss: 1.2856... Generator Loss: 0.9542
Epoch 1/2..., step 1800 Discriminator Loss: 0.9750... Generator Loss: 1.0085
Epoch 1/2..., step 1900 Discriminator Loss: 1.4575... Generator Loss: 0.7632
Epoch 1/2..., step 2000 Discriminator Loss: 0.9871... Generator Loss: 1.2711
Epoch 1/2..., step 2100 Discriminator Loss: 1.2348... Generator Loss: 1.1279
Epoch 1/2..., step 2200 Discriminator Loss: 1.1773... Generator Loss: 0.9368
Epoch 1/2..., step 2300 Discriminator Loss: 1.1326... Generator Loss: 1.1089
Epoch 1/2..., step 2400 Discriminator Loss: 1.2259... Generator Loss: 0.7977
Epoch 1/2..., step 2500 Discriminator Loss: 1.1526... Generator Loss: 0.9398
Epoch 1/2..., step 2600 Discriminator Loss: 1.1525... Generator Loss: 0.8096
Epoch 1/2..., step 2700 Discriminator Loss: 1.1470... Generator Loss: 0.9420
Epoch 1/2..., step 2800 Discriminator Loss: 1.1873... Generator Loss: 0.9342
Epoch 1/2..., step 2900 Discriminator Loss: 1.4252... Generator Loss: 0.8782
Epoch 1/2..., step 3000 Discriminator Loss: 1.2728... Generator Loss: 1.0043
Epoch 1/2..., step 3100 Discriminator Loss: 1.1441... Generator Loss: 0.9901
Epoch 2/2..., step 3200 Discriminator Loss: 1.4410... Generator Loss: 0.8124
Epoch 2/2..., step 3300 Discriminator Loss: 1.3786... Generator Loss: 1.0698
Epoch 2/2..., step 3400 Discriminator Loss: 1.3571... Generator Loss: 0.6668
Epoch 2/2..., step 3500 Discriminator Loss: 1.2780... Generator Loss: 0.8292
Epoch 2/2..., step 3600 Discriminator Loss: 1.2294... Generator Loss: 1.1845
Epoch 2/2..., step 3700 Discriminator Loss: 1.3749... Generator Loss: 0.6481
Epoch 2/2..., step 3800 Discriminator Loss: 1.3979... Generator Loss: 0.5621
Epoch 2/2..., step 3900 Discriminator Loss: 1.4590... Generator Loss: 0.8935
Epoch 2/2..., step 4000 Discriminator Loss: 1.3642... Generator Loss: 0.6727
Epoch 2/2..., step 4100 Discriminator Loss: 1.3573... Generator Loss: 0.7162
Epoch 2/2..., step 4200 Discriminator Loss: 1.4307... Generator Loss: 0.7485
Epoch 2/2..., step 4300 Discriminator Loss: 1.2811... Generator Loss: 0.6205
Epoch 2/2..., step 4400 Discriminator Loss: 1.5033... Generator Loss: 0.5025
Epoch 2/2..., step 4500 Discriminator Loss: 1.2457... Generator Loss: 0.9541
Epoch 2/2..., step 4600 Discriminator Loss: 1.2688... Generator Loss: 0.7536
Epoch 2/2..., step 4700 Discriminator Loss: 1.3442... Generator Loss: 0.8616
Epoch 2/2..., step 4800 Discriminator Loss: 1.3525... Generator Loss: 0.7475
Epoch 2/2..., step 4900 Discriminator Loss: 1.3332... Generator Loss: 0.8861
Epoch 2/2..., step 5000 Discriminator Loss: 1.4264... Generator Loss: 0.7992
Epoch 2/2..., step 5100 Discriminator Loss: 1.3930... Generator Loss: 0.6888
Epoch 2/2..., step 5200 Discriminator Loss: 1.2789... Generator Loss: 0.7493
Epoch 2/2..., step 5300 Discriminator Loss: 1.2285... Generator Loss: 0.8247
Epoch 2/2..., step 5400 Discriminator Loss: 1.3437... Generator Loss: 0.7451
Epoch 2/2..., step 5500 Discriminator Loss: 1.3092... Generator Loss: 0.8291
Epoch 2/2..., step 5600 Discriminator Loss: 1.3924... Generator Loss: 0.6162
Epoch 2/2..., step 5700 Discriminator Loss: 1.4482... Generator Loss: 0.7383
Epoch 2/2..., step 5800 Discriminator Loss: 1.4058... Generator Loss: 0.7263
Epoch 2/2..., step 5900 Discriminator Loss: 1.2653... Generator Loss: 0.7821
Epoch 2/2..., step 6000 Discriminator Loss: 1.3829... Generator Loss: 0.7465
Epoch 2/2..., step 6100 Discriminator Loss: 1.2845... Generator Loss: 0.8295
Epoch 2/2..., step 6200 Discriminator Loss: 1.2322... Generator Loss: 0.8717
Epoch 2/2..., step 6300 Discriminator Loss: 1.2433... Generator Loss: 0.8710

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.